mmclassification/configs/_base_/datasets/ocrvqa.py

82 lines
2.0 KiB
Python

# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys=['question', 'gt_answer']),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=[],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys=['question', 'gt_answer']),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=[],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='OCRVQA',
data_root='data/ocrvqa',
data_prefix='images',
ann_file='annotations/dataset.json',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=64,
num_workers=8,
dataset=dict(
type='OCRVQA',
data_root='data/ocrvqa',
data_prefix='images',
ann_file='annotations/dataset.json',
split='val',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='VQAAcc')
test_dataloader = dict(
batch_size=64,
num_workers=8,
dataset=dict(
type='OCRVQA',
data_root='data/ocrvqa',
data_prefix='images',
ann_file='annotations/dataset.json',
split='test',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
test_evaluator = dict(type='VQAAcc')